During June 2025, Npo focused on backend development for the vllm-spyre repository, addressing a critical issue in the continuous batching model runner. They implemented a fix in Python that enforced a minimum batch size of two for decode operations, introducing input token padding and dynamic memory block allocation when batch sizes fell below this threshold. Their approach included automatic cleanup of padding resources, which reduced peak memory usage and improved production stability for continuous decoding workloads. This work demonstrated skills in batch processing, model optimization, and memory management, resulting in more predictable throughput and efficient resource utilization in production environments.